Instructions to use namesarnav/causalbench_code-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/causalbench_code-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_code-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_code-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_code-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fine-tuned on causal classification
Browse files- eval_results.json +6 -6
eval_results.json
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{
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"eval_loss": 0.
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"eval_accuracy": 0.
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"eval_macro_f1": 0.
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"eval_runtime":
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"eval_samples_per_second":
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"eval_steps_per_second":
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"epoch": 5.0
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}
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{
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"eval_loss": 0.5690733194351196,
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"eval_accuracy": 0.7080152671755725,
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"eval_macro_f1": 0.7029638652718904,
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"eval_runtime": 258.0623,
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"eval_samples_per_second": 6.092,
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"eval_steps_per_second": 0.194,
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"epoch": 5.0
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}
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